An Improved Composite Differential Evolutionary Algorithm with Self-adaptive Mutation Strategy for Identifying Photovoltaic Model Parameters

With the rapid growth of solar energy demand, the optimization of the photovoltaic model becomes significant. The conversion efficiency of the photovoltaic model is mainly determined by its structural parameters, and the multi-modal property of parameter search space brings challenges to the existing evolutionary algorithms. Therefore, this paper proposes an improved composite differential evolutionary algorithm with a self-adaptive mutation strategy (CoDESA). In CoDESA, three complementary strategies are selected into the strategy pool, and each parent will produce three offspring according to their selection probabilities. Moreover, the selection probability of each strategy is dynamically adjusted using a self-adaptive mechanism, so that the algorithm can utilize the more suitable strategies at specific evolutionary stages. The proposed CoDESA is examined on the parameter identification of three photovoltaic models. It is compared with seven commonly used evolutionary algorithms, and more accurate parameters are identified.